No abstract

This article shows the development of an exoskeleton for human joint. The exoskeleton proposed was developed for rehabilitating individuals who have suffered injuries at their shoulders, by rehabilitation exercises. The exoskeleton has special characteristics to deal with the 5 degrees of freedom of the human shoulder. The dynamic model results in the following form: € q = f (q, _ q) + b(q)u, where q, u, f (q, _ q) and b(q) are state's vector, torque's vector, a matrix function and a vector function, respectively. Therefore, we applied four different control laws, among which stand two robust controls (adaptive sliding modes and proportional-derivative with adaptive gravity compensation). The adaptive controller properties allow the exoskeleton to adapt to different humans with different parameters such as size, length, weight and so on that, in mathematical terms, is represented as a mechanical system with uncertainties.

Quantitative gait analysis allows clinicians to assess the inherent gait variability over time which is a functional marker to aid in the diagnosis of disabilities or diseases such as frailty, the onset of cognitive decline and neurodegenerative diseases, among others. However, despite the accuracy achieved by the current specialized systems there are constraints that limit quantitative gait analysis, for instance, the cost of the equipment, the limited access for many people and the lack of solutions to consistently monitor gait on a continuous basis. In this paper, two low-cost systems for quantitative gait analysis are presented, a wearable inertial system that relies on two wireless acceleration sensors mounted on the ankles; and a passive vision-based system that externally estimates the measurements through a structured light sensor and 3D point-cloud processing. Both systems are compared with a reference clinical instrument using an experimental protocol focused on the feasibility of estimating temporal gait parameters over two groups of healthy adults (five elders and five young subjects) under controlled conditions. The error of each system regarding the ground truth is computed. Inter-group and intra-group analyses are also conducted to transversely compare the performance between both technologies, and of these technologies with respect to the reference system. The comparison under controlled conditions is required as a previous stage towards the adaptation of both solutions to be incorporated into Ambient Assisted Living environments and to provide continuous in-home gait monitoring as part of the future work.

This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.